| dc.description.abstract | Nowadays, mechanical ventilations play an important role in the intensive care unit (ICU). It not only help patients with respiratory failure breathe normally, but also maintain the basic respiratory volume which the patients need to let them survive. This study propose a method for calculating the ideal minute volume, and design a complete monitoring system to help paramedics know the patients’ condition immediately. When medical workers use mechanical ventilations, there are many kinds of modes they can choose. Some modes can only control the volume, and some other modes can only control the frequency. By referring to the paper proposed by the team of doctor Wu in 2010, this study survey the Optimal Minute Volume (OMV%) in the Adaptive Support Ventilation(ASV) mode, which is a novel mode that can control both volume and frequency. Besides, this study take OMV% as a new index of patients’ respiratory condition. Since the process of surveying OMV% is time-consuming and need paramedics to tune the knob frequently, it increase the burden of paramedics as well as decrease the medical efficiency. Therefore, this study design a system to become the process of surveying OMV% automatically. We use lung model to produce spontaneous breathe to do the experiment. After that, the system allows the mechanical ventilation to automatically survey OMV% at fixed time every day and present the result at human computer interface for paramedics. Moreover, this system uses the computational strategy in ASV mode to calculate the target minute volume, target tidal volume, target respiratory rate and safety range. These information are shown on the monitoring interface. However, this monitoring system is independent of a ventilator. It can help patient find optimal minute volume. In the end, this study combine digital medical record with this monitoring system to develop a complete optimal respiratory volume monitoring system.
Keyword: Optimal Minute Volume, Mechanical Ventilation Monitoring System, Minute Volume Monitoring | en_US |